# CH391L/HiddenMarkovModel

## Viterbi Algorithm

This is an 'dishonest casino' example from Durbin, et al. 'Biological Sequence Analysis' book. A casino used two types of dice: one is a 'fair' one that has equal chance to all six numbers, and the other is a 'loaded' one that has high chance of number 6 than the others. We have a sequence of dice numbers from the casino, and want to estimate when a dealer exchanged the dice.

We assume that the Hidden Markov Model of this example as below: As you see, both emission probability and transition probability are given.

• [Emission probability]
• Fair: 1/6 1/6 1/6 1/6 1/6 1/6
• Loaded: 0.1 0.1 0.1 0.1 0.1 0.5
• [Transition probability]
• F -> F : 0.95
• F -> L : 0.05
• L -> L : 0.90
• L -> F : 0.10

Assume that we observed the following numbers from the casino: 23653245666

There is no 'begin' state in our model, so we will start our calculation at the second observation (this is the first order HMM, meaning that we need a 'previous state/observation').

First observation '2' has two probabilities:

• 0.167 if it was 'Fair' state
• 0.1 if it was 'Loaded' state.

The second observation '3' has four possible probabilities:

• '2' is 'Fair' & '3' is also 'Fair': 0.167(2 at Fair) * 0.95 (F->F) * 0.167 (3 at Fair).
• '2' is 'Loaded' & '3' is also 'Fair': 0.1(2 at Loaded) * 0.10 (L->F) * 0.167 (3 at Fair).
• '2' is 'Fair' & '3' is also 'Loaded': 0.1(2 at Fair) * 0.05 (F->L) * 0.1 (3 at Loaded).
• '2' is 'Loaded' & '3' is also 'Loaded': 0.167(2 at fair) * 0.90 (L->L) * 0.1 (3 at Loaded).

Among these cases, most favorable path for both Fair and Loaded states is recorded.

Keep counting until the end of observation, then compare two states at the last observation.

• F -> F: 0.167 * 0.95 * 1.65034e-09 = 2.61303e-10
• L -> F: 0.167 * 0.10 * 7.40594e-10 = 1.23432e-11
• F -> L: 0.500 * 0.05 * 1.65034e-09 = 4.12584e-11
• L -> L: 0.500 * 0.90 * 7.40594e-10 = 3.33267e-10

The highest value is for state 'Loaded' from previously 'Loaded' state. So we can start tracing back the whole state path from here.

In this example, the final state prediction output is

• 23653245666 <-- Observation
• FFFFFFFFFLL <-- Predicted state sequence.

Here's also my PERL script for this calculation, and annotated output.

## HMMER

This is the most famous HMM application in biology. Developed by Sean Eddy (the author of many 'primer' series used in our class, and one of co-authors of 'Biological Sequence Analysis' textbook). Many 'sequence-based' domain information is originated from the database called Pfam, and this program is a core of Pfam construction. Also, the program is very mature, well-documented, so you can easily make your own 'profile family' when you have multiple sequence alignments.